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A CAD-BEM geometry transformation method for face-based primary geometric input based on closed contour recognition

  • Research Article
  • Advances in Modeling and Simulation Tools
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Abstract

Performance analysis during the early design stage can significantly reduce building energy consumption. However, it is difficult to transform computer-aided design (CAD) models into building energy models (BEM) to optimize building performance. The model structures for CAD and BEM are divergent. In this study, geometry transformation methods was implemented in BES tools for the early design stage, including auto space generation (ASG) method based on closed contour recognition (CCR) and space boundary topology calculation method. The program is developed based on modeling tools SketchUp to support the CAD format (like *.stl, *.dwg, *.ifc, etc.). It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements. In addition, this study provided a space topology calculation method based on a single-zone BEM output. The program was developed based on the SketchUp modeling tool to support additional CAD formats (such as *.stl, *.dwg, *.ifc), which can then be imported and transformed into *.obj. Compared to current methods mostly focused on BIM-BEM transformation, this method can ensure more modeling flexibility. The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program. They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions. It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.

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Abbreviations

1LSB:

first-level space boundary

2LSB:

second-level space boundary

AFN:

air flow network

ASG:

auto space generation

BEM:

building energy models

BES:

building energy simulation

BIM:

building information model

B-rep:

boundary representation format

BTG:

building topology graph method

CAD:

computer-aided design

CCR:

closed contour recognition method

CP:

cell representation model

CSG:

constructive solid geometry

DRM:

dimensionally reduced model

IFC:

industry foundation classes

SB:

space boundary

A r :

Cr/C (%)

A s :

Sr/S (%)

A wi :

proportion of internal walls (partitions) that were recognized (%)

A wo :

proportion of external walls that were recognized (%)

C :

total rooms/zones of the building

C r :

zones recognized by the program

S :

gross floor area (m2)

S r :

total area of the recognized zones (m2)

T g,i :

program duration for a specific process (data preprocessing, data cleansing, 1LSB generation, space construction and topology calculation) and Tg is the total duration (s)

T r,i :

Tg,i/Cr (s/room)

B i,k :

1LSB representations in the node group Gi,k

C i,j :

list of connected nodes to the node Pi,j

F L :

list of building floors

N i :

list of nodes in level Hi

V i :

list of vectors representing the directions of walls in level Hi

W i :

list of walls in level Hi

References

  • Attia S, Gratia E, De Herde A, et al. (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings, 49: 2–15.

    Article  Google Scholar 

  • Autodesk (2023). Green Building Studio (Version 3.4). Available at https://gbs.autodesk.com/GBS/

  • Bazjanac V (2010). Space boundary requirements for modeling of building geometry for energy and other performance simulation. In: Proceedings of the 27th CIB W78 International Conference, Cairo, Egypt.

  • Bergel R, do Amaral Silva GF, Tillberg M, et al. (2019). Energy performance modeling: Introducing the building early-stage design optimization tool (BeDOT). In: Proceedings of the 16th International IBPSA Building Simulation Conference, Rome, Italy.

  • buildingSMART (2023a). IfcRelSpaceBoundary1stLevel. Available at https://standards.buildingsmart.org/IFC/RELEASE/IFC4/ADD2_TC1/HTML/schema/ifcproductextension/lexical/ifcrelspaceboundary1stlevel.htm

  • buildingSMART. (2023b). IfcRelSpaceBoundary2ndLevel. Available at https://standards.buildingsmart.org/IFC/RELEASE/IFC4/ADD2_TC1/HTML/schema/ifcproductextension/lexical/ifcrelspaceboundary2ndlevel.htm.

  • Chen H, Li Z, Wang X, et al. (2018). A graph- and feature-based building space recognition algorithm for performance simulation in the early design stage. Building Simulation, 11: 281–292.

    Article  Google Scholar 

  • DOE (2023). EnergyPlus (Version 23.1.0). U.S. Department of Energy. Dogan T, Reinhart C (2013). Automated Conversion of Architectural Massing Models Into Thermal’ Shoebox’ Models. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.

  • Dogan T, Reinhart C, Michalatos P (2016). Autozoner: An algorithm for automatic thermal zoning of buildings with unknown interior space definitions. Journal of Building Performance Simulation, 9: 176–189.

    Article  Google Scholar 

  • Drexl T (2003). Entwicklung intelligenter Pfadsuchsysteme für Architekturmodelle am Beispiel eines Kiosksystems (Info-Point) für die FMI in Garching. PhD Thesis, Technische Universität München, Germany. (in German)

    Google Scholar 

  • El-Diraby T, Krijnen T, Papagelis M (2017). BIM-based collaborative design and socio-technical analytics of green buildings. Automation in Construction, 82: 59–74.

    Article  Google Scholar 

  • Fu Y (2022). Research on methods and tool of building ventilation performance analysis for early design stage. Master Thesis, Tsinghua University, China. (in Chinese)

    Google Scholar 

  • Hitchcock RJ, Wong J (2011). Transforming IFC Architectural View Bims for Energy Simulation. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.

  • Huang M, Du Y, Zhang J, et al. (2016). A topological enabled three-dimensional model based on constructive solid geometry and boundary representation. Cluster Computing, 19: 2027–2037.

    Article  Google Scholar 

  • IEA (2018). Annex 30 Thermal Energy Storage for Cost-Effective Energy Management and CO2 Mitigation. International Energy Agency.

  • Jayathissa P, Luzzatto M, Schmidli J, et al. (2017). Optimising building net energy demand with dynamic BIPV shading. Applied Energy, 202: 726–735.

    Article  Google Scholar 

  • Jones NL, McCrone CJ, Walter BJ, et al. (2013). Automated translation and thermal zoning of digital building models for energy analysis. In: Proceedings of the 13th International Conference of the International Building Performance Simulation Association, San Francisco, USA.

  • Ladenhauf D, Battisti K, Berndt R, et al. (2016). Computational geometry in the context of building information modeling. Energy and Buildings, 115: 78–84.

    Article  Google Scholar 

  • Li Z, Chen H, Lin B, et al. (2018). Fast bidirectional building performance optimization at the early design stage. Building Simulation, 11: 647–661.

    Article  Google Scholar 

  • Lilis GN, Giannakis GI, Rovas DV (2017). Automatic generation of second-level space boundary topology from IFC geometry inputs. Automation in Construction, 76: 108–124.

    Article  Google Scholar 

  • Lilis GN, Katsigarakis K, Rovas D (2021). Automatic IFC data enrichment with space geometries for Building Energy Performance Simulations. In: Proceedings of the 17th International IBPSA Building Simulation Conference.

  • Lin B, Chen H, Yu Q, et al. (2021). MOOSAS—A systematic solution for multiple objective building performance optimization in the early design stage. Building and Environment, 200: 107929.

    Article  Google Scholar 

  • Marsault X, Torres F (2019). An interactive and generative eco-design tool for architects in the sketch phase. Journal of Physics: Conference Series, 1343: 012136.

    Google Scholar 

  • Mathur J, Bhatia A (2017). Building Energy Simulation: A Workbook Using DesignBuilder™ (1st Edition). Boca Raton, FL, USA: CRC Press.

    Google Scholar 

  • Openstudio (2023). Openstudio (Version 3.7.0). Available at https://openstudio.net/

  • O’Rourke J (1998). Cambridge Tracts in Theoretical Computer Science. Computational Geometry in C. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Purup PB, Petersen S (2020). Requirement analysis for building performance simulation tools conformed to fit design practice. Automation in Construction, 116: 103226.

    Article  Google Scholar 

  • Rodrigues E, Amaral AR, Gaspar AR, et al. (2015). GerAPlanO—A new building design tool: design generation, thermal assessment and performance optimization. In: Proceedings of Energy for Sustainability 2015 Conference. Sustainable Cities: Designing for People and the Planet, Coimbra, Portugal.

  • Rose CM, Bazjanac V (2015). An algorithm to generate space boundaries for building energy simulation. Engineering with Computers, 31: 271–280.

    Article  Google Scholar 

  • Roudsari MS, Pak M (2013). Ladybug: A parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.

  • Sefaira (2023). Sefaira. Available at https://www.sketchup.com/products/sefaira

  • Solemma (2023). ClimateStudio. Available at https://www.solemma.com/climatestudio

  • Van Treeck C, Rank E (2007). Dimensional reduction of 3D building models using graph theory and its application in building energy simulation. Engineering with Computers, 23: 109–122.

    Article  Google Scholar 

  • Wang C, Lu S, Chen H, et al. (2021). Effectiveness of one-click feedback of building energy efficiency in supporting early-stage architecture design: an experimental study. Building and Environment, 196: 107780.

    Article  Google Scholar 

  • Ying H, Lee S (2021). Generating second-level space boundaries from large-scale IFC-compliant building information models using multiple geometry representations. Automation in Construction, 126: 103659.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the National Science Foundation of China (Grant No. 52130803) for funding this study.

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Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jun Xiao, Hao Zhou, Shiji Yang, Deyin Zhang, Borong Lin. The first draft of the manuscript was written by Jun Xiao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Borong Lin.

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The authors have no competing interests to declare that are relevant to the content of this article. Borong Lin is an editorial board member of Building Simulation.

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Xiao, J., Zhou, H., Yang, S. et al. A CAD-BEM geometry transformation method for face-based primary geometric input based on closed contour recognition. Build. Simul. 17, 335–354 (2024). https://doi.org/10.1007/s12273-023-1081-6

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  • DOI: https://doi.org/10.1007/s12273-023-1081-6

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